ArticleCancers2022
Metabolomics by NMR Combined with Machine Learning to Predict Neoadjuvant Chemotherapy Response for Breast Cancer.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 24 citations in OpenAlex.
- Metabolomics in breast cancer: insights into treatment responses, disease progression, and prognostic assessment.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Use of Nuclear Magnetic Resonance (NMR) to Study Cancer Metabolism.Advances in experimental medicine and biology · 2026Review
- Identification of predictive pretreatment biomarkers for neoadjuvant chemotherapy response in Latino invasive breast cancer patients.Molecular medicine (Cambridge, Mass.) · 2025Article
- Metabolic Reprogramming in Retinoblastoma Patients: Treatment Naïve Active Retinoblastoma Versus Regressed Tumors Post-Treatment.Investigative ophthalmology & visual science · 2025Article
- Metabolomics as a tool for understanding and treating triple-negative breast cancer.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Resistance to neoadjuvant chemotherapy in breast cancers: a metabolic perspective.Journal of experimental & clinical cancer research : CR · 2025Review
- Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling.Metabolites · 2025Review
- Interpretable Machine Learning for Serum-Based Metabolomics in Breast Cancer Diagnostics: Insights from Multi-Objective Feature Selection-Driven LightGBM-SHAP Models.Medicina (Kaunas, Lithuania) · 2025Article
- Application of Metabolic Biomarkers in Breast Cancer: A Literature Review.Annals of laboratory medicine · 2025Review
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- Serum metabolomic profiling for predicting therapeutic response and toxicity in breast cancer neoadjuvant chemotherapy: a retrospective longitudinal study.Breast cancer research : BCR · 2025Article
- AI Applications to Breast MRI: Today and Tomorrow.Journal of magnetic resonance imaging : JMRI · 2024Review
- Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics.Breast cancer research and treatment · 2024Article
- Metabolomic Profiling of Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy for Predicting Disease-Free and Overall Survival.International journal of molecular sciences · 2024Article
- Radiology and multi-scale data integration for precision oncology.NPJ precision oncology · 2024Review
- Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer.Cancers · 2024Article
- Metabolomics of 3D cell co-culture reveals alterations in energy metabolism at the cross-talk of colorectal cancer-adipocytes.Frontiers in medicine · 2024Article
- Metabolic signature ofFrontiers in veterinary science · 2023Article
- Multiomics insights on the onset, progression, and metastatic evolution of breast cancer.Frontiers in oncology · 2023Review
Corrections and comments
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Authors and funding
21 authors at 6 institutions in 3 countries.
Funding
Abstract
Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.